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Presenters:

Companies are doing a better and better job of collecting data that explains why consumers behave the way they do. These diverse data sets cause us to rethink some of the workhorse algorithms for data analysis. Specifically, the traditional binary response model leaves much room for improvement in how it embraces time. Cross–sectional models allow much rich data to fall through the cracks. We’ll discuss real-world scenarios and how to better use data with time to event modeling.

This session will cover:

Several business scenarios where time to event modeling makes better use of rich data.

Time to event models for prediction

Time to event models for inference

RevoScale functions used for data analysis

About the speakers:

About the Speakers:

Speaker photo

About the speaker

John is a data geek who writes left-handed but bats right-handed. Always the entrepreneur, he put himself through school by farming soybeans, waiting tables, and finding a paying client that doubled as an MBA internship project. In 2003, John successfully founded the analytic consulting firm that has grown into DataSong and led its expansion into big data applications for customer buying intelligence. He was previously with the SAS Institute Analytical Consulting group, which he's glad he worked at… and also glad he left. John holds an MBA in Decision Science from George Washington University.

Tess is a Statistician with experience in R, SAS and Stata statistical packages. She has managed and analyzed large databases, designed and analyzed clinical trial experiments, and developed customized solutions to solve complex statistical and prediction problems. She holds a B.A. in Mathematics from Santa Clara University and a Ph.D. in Statistics from University of California, Los Angeles.